Network modeling reveals prevalent negative regulatory relationships between signaling sectors in Arabidopsis immune signaling.

Biological signaling processes may be mediated by complex networks in which network components and network sectors interact with each other in complex ways. Studies of complex networks benefit from approaches in which the roles of individual components are considered in the context of the network. T...

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Autores principales:	Masanao Sato, Kenichi Tsuda, Lin Wang, John Coller, Yuichiro Watanabe, Jane Glazebrook, Fumiaki Katagiri
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2010
Materias:	Immunologic diseases. Allergy RC581-607 Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/057f6235e0194c968096271ad9c80b6d
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spelling	oai:doaj.org-article:057f6235e0194c968096271ad9c80b6d2021-12-02T20:00:27ZNetwork modeling reveals prevalent negative regulatory relationships between signaling sectors in Arabidopsis immune signaling.1553-73661553-737410.1371/journal.ppat.1001011https://doaj.org/article/057f6235e0194c968096271ad9c80b6d2010-07-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/20661428/?tool=EBIhttps://doaj.org/toc/1553-7366https://doaj.org/toc/1553-7374Biological signaling processes may be mediated by complex networks in which network components and network sectors interact with each other in complex ways. Studies of complex networks benefit from approaches in which the roles of individual components are considered in the context of the network. The plant immune signaling network, which controls inducible responses to pathogen attack, is such a complex network. We studied the Arabidopsis immune signaling network upon challenge with a strain of the bacterial pathogen Pseudomonas syringae expressing the effector protein AvrRpt2 (Pto DC3000 AvrRpt2). This bacterial strain feeds multiple inputs into the signaling network, allowing many parts of the network to be activated at once. mRNA profiles for 571 immune response genes of 22 Arabidopsis immunity mutants and wild type were collected 6 hours after inoculation with Pto DC3000 AvrRpt2. The mRNA profiles were analyzed as detailed descriptions of changes in the network state resulting from the genetic perturbations. Regulatory relationships among the genes corresponding to the mutations were inferred by recursively applying a non-linear dimensionality reduction procedure to the mRNA profile data. The resulting static network model accurately predicted 23 of 25 regulatory relationships reported in the literature, suggesting that predictions of novel regulatory relationships are also accurate. The network model revealed two striking features: (i) the components of the network are highly interconnected; and (ii) negative regulatory relationships are common between signaling sectors. Complex regulatory relationships, including a novel negative regulatory relationship between the early microbe-associated molecular pattern-triggered signaling sectors and the salicylic acid sector, were further validated. We propose that prevalent negative regulatory relationships among the signaling sectors make the plant immune signaling network a "sector-switching" network, which effectively balances two apparently conflicting demands, robustness against pathogenic perturbations and moderation of negative impacts of immune responses on plant fitness.Masanao SatoKenichi TsudaLin WangJohn CollerYuichiro WatanabeJane GlazebrookFumiaki KatagiriPublic Library of Science (PLoS)articleImmunologic diseases. AllergyRC581-607Biology (General)QH301-705.5ENPLoS Pathogens, Vol 6, Iss 7, p e1001011 (2010)
institution	DOAJ
collection	DOAJ
language	EN
topic	Immunologic diseases. Allergy RC581-607 Biology (General) QH301-705.5
spellingShingle	Immunologic diseases. Allergy RC581-607 Biology (General) QH301-705.5 Masanao Sato Kenichi Tsuda Lin Wang John Coller Yuichiro Watanabe Jane Glazebrook Fumiaki Katagiri Network modeling reveals prevalent negative regulatory relationships between signaling sectors in Arabidopsis immune signaling.
description	Biological signaling processes may be mediated by complex networks in which network components and network sectors interact with each other in complex ways. Studies of complex networks benefit from approaches in which the roles of individual components are considered in the context of the network. The plant immune signaling network, which controls inducible responses to pathogen attack, is such a complex network. We studied the Arabidopsis immune signaling network upon challenge with a strain of the bacterial pathogen Pseudomonas syringae expressing the effector protein AvrRpt2 (Pto DC3000 AvrRpt2). This bacterial strain feeds multiple inputs into the signaling network, allowing many parts of the network to be activated at once. mRNA profiles for 571 immune response genes of 22 Arabidopsis immunity mutants and wild type were collected 6 hours after inoculation with Pto DC3000 AvrRpt2. The mRNA profiles were analyzed as detailed descriptions of changes in the network state resulting from the genetic perturbations. Regulatory relationships among the genes corresponding to the mutations were inferred by recursively applying a non-linear dimensionality reduction procedure to the mRNA profile data. The resulting static network model accurately predicted 23 of 25 regulatory relationships reported in the literature, suggesting that predictions of novel regulatory relationships are also accurate. The network model revealed two striking features: (i) the components of the network are highly interconnected; and (ii) negative regulatory relationships are common between signaling sectors. Complex regulatory relationships, including a novel negative regulatory relationship between the early microbe-associated molecular pattern-triggered signaling sectors and the salicylic acid sector, were further validated. We propose that prevalent negative regulatory relationships among the signaling sectors make the plant immune signaling network a "sector-switching" network, which effectively balances two apparently conflicting demands, robustness against pathogenic perturbations and moderation of negative impacts of immune responses on plant fitness.
format	article
author	Masanao Sato Kenichi Tsuda Lin Wang John Coller Yuichiro Watanabe Jane Glazebrook Fumiaki Katagiri
author_facet	Masanao Sato Kenichi Tsuda Lin Wang John Coller Yuichiro Watanabe Jane Glazebrook Fumiaki Katagiri
author_sort	Masanao Sato
title	Network modeling reveals prevalent negative regulatory relationships between signaling sectors in Arabidopsis immune signaling.
title_short	Network modeling reveals prevalent negative regulatory relationships between signaling sectors in Arabidopsis immune signaling.
title_full	Network modeling reveals prevalent negative regulatory relationships between signaling sectors in Arabidopsis immune signaling.
title_fullStr	Network modeling reveals prevalent negative regulatory relationships between signaling sectors in Arabidopsis immune signaling.
title_full_unstemmed	Network modeling reveals prevalent negative regulatory relationships between signaling sectors in Arabidopsis immune signaling.
title_sort	network modeling reveals prevalent negative regulatory relationships between signaling sectors in arabidopsis immune signaling.
publisher	Public Library of Science (PLoS)
publishDate	2010
url	https://doaj.org/article/057f6235e0194c968096271ad9c80b6d
work_keys_str_mv	AT masanaosato networkmodelingrevealsprevalentnegativeregulatoryrelationshipsbetweensignalingsectorsinarabidopsisimmunesignaling AT kenichitsuda networkmodelingrevealsprevalentnegativeregulatoryrelationshipsbetweensignalingsectorsinarabidopsisimmunesignaling AT linwang networkmodelingrevealsprevalentnegativeregulatoryrelationshipsbetweensignalingsectorsinarabidopsisimmunesignaling AT johncoller networkmodelingrevealsprevalentnegativeregulatoryrelationshipsbetweensignalingsectorsinarabidopsisimmunesignaling AT yuichirowatanabe networkmodelingrevealsprevalentnegativeregulatoryrelationshipsbetweensignalingsectorsinarabidopsisimmunesignaling AT janeglazebrook networkmodelingrevealsprevalentnegativeregulatoryrelationshipsbetweensignalingsectorsinarabidopsisimmunesignaling AT fumiakikatagiri networkmodelingrevealsprevalentnegativeregulatoryrelationshipsbetweensignalingsectorsinarabidopsisimmunesignaling
_version_	1718375701029060608

Network modeling reveals prevalent negative regulatory relationships between signaling sectors in Arabidopsis immune signaling.

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